Thinking With Containers: A Multi-Agent Retrieval Approach for the Case-Based Semantic Search of Architectural Designs

Ayzenshtadt, Viktor; Langenhan, Christoph; Bukhari, Syed Saqib; Althoff, Klaus-Dieter; Petzold, Frank; Dengel, Andreas

doi:10.5220/0005683801490156

Cited by 13 publications

(6 citation statements)

References 8 publications

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“…Jaiswal et al [14] provide a study about how wireless sensor and actuator networks are used to remotely monitor and control the environment according to the decisions made by the centralized reasoner, in which a CBR agent is involved. Ayzenshtadt et al [36] present a distributed retrieval system, MetisCBR, for the building (including electrical issues) design domain, where agents work in groups (containers) on resolving of user queries built with a semantic description model Semantic Fingerprint. The main aim of this approach is to carry out a basis for a considerable retrieval tool for architects, where the combination of CBR and MAS helps to achieve valuable and helpful search results in a comprehensive building design collection.…”

Section: Energy Management Using Cbr Systemsmentioning

confidence: 99%

Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings

Pinto

Faia

Navarro-Cáceres

et al. 2019

IEEE Systems Journal

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This paper proposes a novel case-based reasoning (CBR) recommender system for intelligent energy management in buildings. The proposed approach recommends the amount of energy reduction that should be applied in a building in each moment, by learning from previous similar cases. The k-nearest neighbor clustering algorithm is applied to identify the most similar past cases, and an approach based on support vector machines is used to optimize the weight of different parameters that characterize each case. An expert system composed by a set of ad hoc rules guarantees that the solution is adequate and applicable to the new case scenario. The proposed CBR methodology is modeled through a dedicated software agent, thus enabling its integration in a multi-agent systems society for the study of energy systems. Results show that the proposed approach is able to provide suitable recommendations on energy reduction, by comparing its results with a previous approach based on particle swarm optimization and with the real reduction in past cases. The applicability of the proposed approach in real scenarios is also assessed through the application of the results provided by the proposed approach on a house energy resources management system. Index Terms-Building energy management, case-based reasoning (CBR), energy efficiency, multi-agent systems (MAS).

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Section: Energy Management Using Cbr Systemsmentioning

confidence: 99%

Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings

Pinto

Faia

Navarro-Cáceres

et al. 2019

IEEE Systems Journal

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“…Case-based methods include the retrieval approach MetisCBR [13], which is based on a distributed, ontologically supported search procedure.…”

Section: Data Modelsmentioning

confidence: 99%

“…In this test, all approaches showed a basic readiness Coordinator [17] (Fig. 4) and the rule-and case-based coordination component of the retrieval approach MetisCBR described in [13]. Subjectively evaluated are, according to a questionnaire, the result sets for two types of queries: contiguous (i.e.…”

Section: Data Modelsmentioning

confidence: 99%

See 1 more Smart Citation

Design Support to steer Creative Wicked Problem Solving Processes with Knowledge Management and Artificial Intelligence

Langenhan

Eisenstadt

Petzold

et al. 2019

JGRC

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As the complexity of building tasks and requirements increases, designers often find themselves confronted with interdisciplinary problems that go beyond the specific challenges and methods of architecture. The iterative nature of the design process results in a continuous exchange between creative, analytical and evaluative activities, through which the designer explores and identifies promising design variants. The ability to compare and evaluate relevant reference examples of already built or designed buildings helps designers to assess their own design and informs the design process.

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“…Krauß et al [3] developed the pART workbench, on which architects can touch on the screen and realize mixed query of cases based on building plans. Ayzenshtadt et al [4,5] proposed a case-based semantic retrieval method based on case reasoning and multi-agent system (MAS). In addition to setting up a BCL, Meng [6] presented a method for building scheme retrieval and reuse based on case reasoning.…”

Section: Introductionmentioning

confidence: 99%

Innovation Strategy Generation for Building Design Based on the Optimization Algorithm for Dynamic Sorting of Extension Set

Guo¹,

Fu²,

Li³

2020

RIA

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This paper attempts to improve the utilization efficiency of online building data in the age of big data. Focusing on data-driven building design innovation, the authors created an innovation strategy generation method for building design based on the optimization algorithm for dynamic sorting of extension set. Firstly, online building cases were captured by data acquisition software, and processed into a building case library (BCL). Next, the sorting algorithm and operation mechanism of building cases were optimized by setting up a transform mechanism for building problems and query conditions, preliminary screening of building cases based on correlation function, dynamic sorting of building cases based on extension set, etc. Finally, the innovation strategies for building design were automatically generated from the BCL, and the main factors affecting the case sorting were corrected in detailed. The case study shows that our method has high operability, improves the ability of architects to make scientific decisions based on online data, and thus promotes the innovation of computer-aided building design.

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Thinking With Containers: A Multi-Agent Retrieval Approach for the Case-Based Semantic Search of Architectural Designs

Cited by 13 publications

References 8 publications

Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings

Multi-Agent-Based CBR Recommender System for Intelligent Energy Management in Buildings

Design Support to steer Creative Wicked Problem Solving Processes with Knowledge Management and Artificial Intelligence

Innovation Strategy Generation for Building Design Based on the Optimization Algorithm for Dynamic Sorting of Extension Set

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